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Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup

T0 review · 1 major / 1 minor · reviewed 2026-07-03 · grok-4.3

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Pith's one-line read A gaming platform and 340 GB dataset evaluates social engineering risks in interactions with AI agents via biometrics and controlled games.

desk verdict Releases a new open multimodal dataset from LLM-mediated games with biometrics, but provides no evidence that the setup measures social engineering risks. read the letter →

arxiv 2606.17793 v2 pith:EHNLRGPR submitted 2026-06-16 cs.HC cs.DB

Roberto Daza , Javier Irigoyen , Ivan Lopez , Raquel Rodriguez-Carvajal , Laura Gomez-Carbajo , Julian Fierrez , Ruben Tolosana , Aythami Morales This is my paper · ORCID
classification cs.HCcs.DB
keywords socialengineeringAIrisksmultimodaldatasetgamingplatformbiometricsLLMinteractionPrisoner'sDilemma
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces AIriskEval-gaming as an open platform that runs human-AI and other interaction settings inside two concatenated games: an adapted Prisoner's Dilemma and an Ultimatum Game. Role-conditioned LLM agents interact with 15 participants while six synchronized data streams capture logs, video, gaze, smartwatch signals, and metadata. The resulting dataset supplies interaction paths, psychological profiles, game outcomes, and derived behavioral and biometric features for analysis. Descriptive statistics characterize the collected multimodal signals. The work positions this resource as a means to identify vulnerabilities that affect secure AI deployment.

What carries the argument

AIriskEval-gaming platform, which integrates configurable game templates, role-conditioned LLM agents, psychology-informed profiling, and synchronized multimodal data streams for risk evaluation.

What would settle it

A direct comparison study in which the same participants show no distinguishable biometric or behavioral patterns when exposed to actual social-engineering prompts outside the game setting versus neutral prompts would indicate the platform does not capture the targeted risks.

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Extended reading notes

Core claim

The paper establishes AIriskEval-gaming as a configurable platform and accompanying dataset that supports controlled evaluation of social engineering risks in LLM-mediated multimodal interaction by combining game templates, role-conditioned agents, participant profiling, structured interaction trees, and synchronized acquisition of behavioral and biometric streams.

Load-bearing premise

Gameplay in the adapted Prisoner's Dilemma and Ultimatum Game with role-conditioned AI agents, together with the recorded biometric streams, serves as a valid proxy for social engineering risks that occur in wider real-world AI interactions.

Editorial extensions

If this is right

  • Enables systematic identification of vulnerabilities in AI systems before wider deployment.
  • Provides data to support protection of sensitive information during AI interactions.
  • Facilitates compliance checks against evolving regulatory requirements for AI security.
  • Allows comparative testing across human-human, human-AI, and AI-AI configurations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Patterns extracted from the six data streams may later serve as early indicators for training detection models that flag manipulation attempts in live AI conversations.
  • The platform's structure could be reused with additional participant cohorts to test whether biometric signatures generalize beyond the initial 15-person sample.
  • AI-AI game sessions in the dataset might surface interaction dynamics that differ from human-AI cases and warrant separate risk modeling.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The manuscript introduces AIriskEval-gaming, an open platform and the AIriskEval-gaming-db dataset collected from 15 participants who played concatenated adapted Prisoner's Dilemma and Ultimatum Game scenarios against role-conditioned GPT-5.4 agents. It supplies 340 GB of synchronized multimodal streams (interaction logs, video, gaze, smartwatch, facial features) plus descriptive analyses of paths, outcomes, psychological profiles, and derived features, positioning the resource as enabling evaluation of social engineering risks in LLM-mediated interactions across human-human, human-AI, and AI-AI settings.

Significance. If the proxy validity of the chosen games and biometric streams for social engineering behaviors can be established, the open release of configurable templates, structured interaction trees, and this large multimodal dataset on GitHub would constitute a useful contribution to HCI and AI-safety research by supporting controlled, reproducible studies of biometric and behavioral signals. The explicit provision of raw/processed data and code is a clear strength that facilitates follow-on work.

major comments (1)
  1. [Abstract] Abstract and dataset description: The central claim that the platform and dataset enable 'rigorous risk evaluation' of social engineering risks rests on the untested assumption that behaviors and signals observed in the adapted Prisoner's Dilemma and Ultimatum Game with role-conditioned LLM agents serve as valid proxies for real-world tactics such as manipulation or trust exploitation. Only descriptive statistics on 15 participants are supplied; no expert annotation, external validation, or mapping to SE-relevant outcomes is presented to support this link.
minor comments (1)
  1. [Dataset description] The description of the six data streams and deep-learning-based feature extraction would benefit from an explicit table listing per-stream sampling rates, synchronization method, and any filtering steps applied before feature extraction.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on the positioning of our contribution. The work introduces an open platform and multimodal dataset using established game-theoretic scenarios to support research on social engineering risks in LLM-mediated interactions. We address the major comment below by clarifying scope and committing to revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract and dataset description: The central claim that the platform and dataset enable 'rigorous risk evaluation' of social engineering risks rests on the untested assumption that behaviors and signals observed in the adapted Prisoner's Dilemma and Ultimatum Game with role-conditioned LLM agents serve as valid proxies for real-world tactics such as manipulation or trust exploitation. Only descriptive statistics on 15 participants are supplied; no expert annotation, external validation, or mapping to SE-relevant outcomes is presented to support this link.

    Authors: We agree that the manuscript supplies only descriptive statistics and does not include expert annotation, external validation, or explicit mappings from observed behaviors to real-world social engineering outcomes. The central positioning is that the platform and dataset provide controlled, configurable game templates (adapted Prisoner's Dilemma and Ultimatum Game), role-conditioned agents, structured interaction trees, and synchronized multimodal streams to enable future researchers to conduct such evaluations across human-human, human-AI, and AI-AI settings. These games were selected because prior literature has linked them to constructs such as trust, cooperation, and exploitation, but we do not assert that the collected signals constitute validated proxies. The abstract's phrasing that the resource enables 'rigorous risk evaluation' can be read as overstating immediate applicability. We will revise the abstract, introduction, and dataset description sections to state explicitly that the contribution supplies a foundational resource and data for subsequent validation studies rather than performing or claiming to have performed that validation. This change will be incorporated in the revised manuscript. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Dataset release paper with no derivations, predictions, or self-referential steps

full rationale

The paper introduces an open platform and dataset (AIriskEval-gaming) for collecting multimodal interaction data in adapted games with LLM agents. No equations, model fitting, predictions, or derivations are present in the abstract or described content. The work consists of platform description, data collection from 15 participants, and descriptive statistics; the central claim is the resource release itself rather than any computed result that could reduce to its inputs. No self-citations, ansatzes, or uniqueness claims are invoked as load-bearing elements. This is a standard non-circular dataset paper.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The contribution rests on a domain assumption about the diagnostic value of the chosen games and biometrics rather than new free parameters or invented entities; no fitted constants or theoretical derivations appear in the abstract.

assumptions (1)
  • domain assumption Multimodal biometric and behavioral signals collected during role-conditioned game interactions can serve as indicators of social engineering vulnerabilities in AI-mediated exchanges
    This premise justifies the platform's design for risk evaluation as stated in the abstract.

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Cite this review

Pith. "Pith review of Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup." pith.science (2026). https://pith.science/paper/EHNLRGPR

@misc{pith2026260617793,
  author       = {Pith},
  title        = {Pith review of: Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EHNLRGPR}},
  note         = {Machine review of arXiv:2606.17793}
}
read the original abstract

We introduce AIriskEval-gaming, an open platform and dataset to evaluate social engineering risks in LLM-mediated multimodal interaction through controlled games. It supports human-human, human-AI and AI-AI settings, combining configurable game templates, role-conditioned LLM agents, psychology-informed participant profiling, structured interaction trees, and synchronized behavioral and biometric acquisition, filtering, and deep-learning-based feature extraction. The dataset (AIriskEval-gaming-db) was collected from 15 participants who interacted with a role-conditioned GPT-5.4 agent in two concatenated games: an adapted Prisoner's Dilemma and an Ultimatum Game. It comprises 340 GB of raw and processed multimodal data across six streams: interaction logs, video, screen recordings, gaze logs, smartwatch signals, and game/questionnaire metadata. These data include interaction paths, written justifications, psychological profiles, subjective feedback, perceived counterpart identity, game outcomes, and derived behavioral, facial, and gaze features. Alongside the dataset, we provide descriptive analyses characterizing the resulting multimodal data. Rigorous risk evaluation is essential for the deployment of secure AI systems, as it enables the identification and mitigation of vulnerabilities, ensures the protection of sensitive data, and supports compliance with evolving regulatory and ethical standards in society. The dataset and related code are available on GitHub.

Figures

Figures reproduced from arXiv: 2606.17793 by the authors.

Figure 1
Figure 1. Overview of the ARES platform and pilot dataset. The figure illustrates the main contributions of the paper: (i) the ARES platform; (ii) psychology [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Simplified view of the game template and interaction tree used in the ARES pilot dataset. The adapted Prisoner’s Dilemma is shown at the top, and [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 2
Figure 2. Simplified view of the game template and interaction tree used in the AIriskEval-gaming dataset. The adapted Prisoner’s Dilemma is shown at the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

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